In more realistic scenarios, coping with a variable number of sub-routines per trajectory adds an additional layer of complexity when addressing the problem of unsupervised sequence decomposition holistically.
In CompILE and OMPN, this issue is circumvented by assuming that the number of sub-routines in each trajectory is known in advance, either at training or at test time, which limits their applicability.
In contrast, in SloTTAr we cast the problem of estimating the number of sub-routines as a form of adaptive computation (where each available slot corresponds to an available computational step), requiring it to learn to use only some adaptive subset of all the slots available based on an estimated prior.
This motivates us to use the adaptive computation loss introduced in PonderNet [1]} as our learning objective.
It allows for only an adaptive subset of the available slots in SloTTAr to be actively involved in the decomposition of any trajectory.
